Method and system for trip classification

ABSTRACT

Reckless behavior of drivers like, speeding, sudden acceleration and swerving through lanes can cause fatality and financial loss. Conventional methods mainly focus on driving style classification. The conventional methods mainly focus on driver classification and are not able to provide trip classification of a driver. Hence there is a challenge in trip classification of the driver based on acceleration data. The present disclosure for trip classification addresses the problem of end to end trip classification based on the acceleration data. Here, a journey is segmented into a plurality of sub-journey segments and each sub-journey segment is associated with a plurality of driving events. An event score is calculated for each sub-journey and a normalization is performed on the event score. Further, the journey is classified into at least one of good, average or bad based on the normalized data by utilizing a fuzzy based classification.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 201921040833, filed on Oct. 9, 2019. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

The disclosure herein generally relates to the field of classification,and, more particular, to a method and system for trip classification.

BACKGROUND

Vehicle accidents are common nowadays and large number of vehicleaccidents are closely related to driver behavior and their drivingstyles. Vehicle accidents cause fatalities and injuries, financiallosses and lost productivity. They also result in legal and insurancecosts. Reckless behavior of the driver like, speeding, suddenacceleration and swerving through lanes put other drivers also at risk.Surveys shows that recognizing dangerous driving behavior can be astrong motivator for drivers to improve their driving behavior. However,recognizing the dangerous behavior is a challenging task. However, thesafety of a trip can be assessed by referring to classified trips of thedriver if any and the driver can improve his further driving style basedon that.

Conventional methods mainly focus on driving style classification. Theconventional driving style classification is either performed by usingmultiple sensor data or maneuver based classification. Further, theconventional methods are not able to provide trip classification of adriver and there is a challenge in classifying the trip of the driverbased on acceleration data alone.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a method for trip classification is provided. The methodincludes receiving, a plurality of driving data pertaining to a journeyfrom a plurality of Global Positioning System (GPS) sensors, wherein theplurality of driving data comprising a timestamp, a lateral accelerationof a vehicle, a longitudinal acceleration of the vehicle and a pluralityof traffic information associated with the journey. The method furtherincludes segmenting the journey into a plurality of sub-journeys basedon the plurality of traffic information. Further, the method includesdetecting a plurality of driving events for each of the plurality ofsub-journeys based on the lateral and longitudinal acceleration.Further, the method includes computing an event score associated witheach of the plurality of driving events corresponding to eachsub-journey based on the driving data. Furthermore, the method includesnormalizing the event score corresponding to the plurality of drivingevents based on a percentile mapping, wherein the percentile mapping isperformed by comparing the event score and a global statistics. Finally,the method includes classifying, the journey into at least one of verygood, good, average, bad and very bad, based on the normalized eventscore by utilizing fuzzy classification.

In another aspect, a system for trip classification is provided. Thesystem includes a computing device wherein the computing deviceincludes, at least one memory comprising programmed instructions, atleast one hardware processor operatively coupled to the at least onememory, wherein the at least one hardware processor is capable ofexecuting the programmed instructions stored in the at least onememories to receive a plurality of driving data pertaining to a journeyfrom a plurality of Global Positioning System (GPS) sensors, wherein theplurality of driving data comprising a timestamp, a lateral accelerationof a vehicle, a longitudinal acceleration of the vehicle and a pluralityof traffic information associated with the journey. Further, the one ormore hardware processor is configured to segment the journey into aplurality of sub-journeys based on the plurality of traffic information.Further, the one or more hardware processor is configured to detect aplurality of driving events for each of the plurality of sub-journeysbased on the lateral and longitudinal acceleration. Further, the one ormore hardware processor is configured to compute an event scoreassociated with each of the plurality of driving events corresponding toeach sub-journey based on the driving data. Furthermore, the one or morehardware processor is configured to normalize the event scorecorresponding to the plurality of driving events based on a percentilemapping, wherein the percentile mapping is performed by comparing theevent score and a global statistics. Finally, the one or more hardwareprocessor is configured to classify, the journey into at least one ofvery good, good, average, bad and very bad, based on the normalizedevent score by utilizing fuzzy classification

In yet another aspect, a computer program product comprising anon-transitory computer-readable medium having the trip analysis unit isconfigured to embodied therein a computer program for method and systemfor trip classification is provided. The computer readable program, whenexecuted on a computing device, causes the computing device to receive aplurality of driving data pertaining to a journey from a plurality ofGlobal Positioning System (GPS) sensors, wherein the plurality ofdriving data comprising a timestamp, a lateral acceleration of avehicle, a longitudinal acceleration of the vehicle and a plurality oftraffic information associated with the journey. Further, the computerreadable program, when executed on a computing device, causes thecomputing device to segment the journey into a plurality of sub-journeysbased on the plurality of traffic information. Further, the computerreadable program, when executed on a computing device, causes thecomputing device to detect a plurality of driving events for each of theplurality of sub-journeys based on the lateral and longitudinalacceleration. Further, the computer readable program, when executed on acomputing device, causes the computing device to compute an event scoreassociated with each of the plurality of driving events corresponding toeach sub-journey based on the driving data. Furthermore, the computerreadable program, when executed on a computing device, causes thecomputing device to normalize the event score corresponding to theplurality of driving events based on a percentile mapping, wherein thepercentile mapping is performed by comparing the event score and aglobal statistics. Finally, the computer readable program, when executedon a computing device, causes the computing device to classify, thejourney into at least one of very good, good, average, bad and very bad,based on the normalized event score by utilizing fuzzy classification

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles:

FIG. 1 is a functional block diagram of a system for tripclassification, according to some embodiments of the present disclosure.

FIGS. 2 and 3 depicts membership function plot for a method of tripclassification, in accordance with some embodiments of the presentdisclosure.

FIGS. 4 and 5 depicts density plots for the method for tripclassification, in accordance with some embodiments of the presentdisclosure.

FIG. 6 is a flow diagram for the processor implemented method for tripclassification, in accordance with some embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. It is intendedthat the following detailed description be considered as exemplary only,with the true scope and spirit being indicated by the following claims.For brevity of description, the terms “trip” and “journey” are usedinterchangeably throughout the document.

Embodiments herein provide a method and system for trip classification.The system for trip classification classifies an end to end trip/journeyof a driver into good or bad based on the global database. The systemfor trip classification addresses the problem of end to end tripclassification based on acceleration data. Here, a journey is segmentedinto a plurality of sub-journey segments and each sub-journey segment isassociated with a plurality of driving events. An event score iscalculated for each sub-journey and a normalization is performed on theevent score. Further, the journey is classified into at least one ofgood, average or bad based on the normalized data by utilizing a fuzzybased classification. The journey classification data can be furtherused by a subject to select a driver for a trip and the driver canimprove his/her driving style based on the trip classification. Animplementation of the method and system for trip classification isdescribed further in detail with reference to FIGS. 1 through 6.

Referring now to the drawings, and more particularly to FIG. 1 through6, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1 is a functional block diagram of a system for tripclassification, according to some embodiments of the present disclosure.The system 100 includes or is otherwise in communication with one ormore hardware processors, such as a processors 102, at least one memorysuch as a memory 104, an I/O interface 122. The memory 104 may includethe trip analysis unit 120. The processor 102, memory 104, and the I/Ointerface 122 may be coupled by a system bus such as a system bus 108 ora similar mechanism.

The I/O interface 122 may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,and the like. The I/O interface 122 may include a variety of softwareand hardware interfaces, for example, interfaces for peripheraldevice(s), such as a keyboard, a mouse, an external memory, a pluralityof Global Positioning System (GPS) sensors, a printer and the like.Further, the I/O interface 122 may enable the system 100 to communicatewith other devices, such as web servers and external databases.

The interface 122 can facilitate multiple communications within a widevariety of networks and protocol types, including wired networks, forexample, local area network (LAN), cable, etc., and wireless networks,such as Wireless LAN (WLAN), cellular, or satellite. For the purpose,the interface 122 may include one or more ports for connecting a numberof computing systems with one another or to another server computer. TheI/O interface 122 may include one or more ports for connecting a numberof devices to one another or to another server.

The hardware processor 102 may be implemented as one or moremicroprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, logic circuitries,and/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the hardware processor 102 isconfigured to fetch and execute computer-readable instructions stored inthe memory 104.

The memory 104 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes. In an embodiment, the memory 104 includes a plurality ofmodules 106 and a repository 110 for storing data processed, received,and generated by one or more of the modules 106 and the image analysisunit 120. The modules 106 may include routines, programs, objects,components, data structures, and so on, which perform particular tasksor implement particular abstract data types.

The memory 104 also includes a repository 110. The module(s) 106 includeprograms or coded instructions that supplement applications or functionsperformed by the system 100 for trip classification. The modules 106,amongst other things, can include routines, programs, objects,components, and data structures, which perform particular tasks orimplement particular abstract data types. The modules 106 may also beused as, signal processor(s), state machine(s), logic circuitries,and/or any other device or component that manipulates signals based onoperational instructions. Further, the modules 106 can be used byhardware, by computer-readable instructions executed by a processingunit, or by a combination thereof. The modules 106 can include varioussub-modules (not shown). The modules 106 may include computer-readableinstructions that supplement applications or functions performed by thesystem 100 for trip classification.

The repository 110 may include acceleration data, a global trafficdatabase and other data. Further, the other data amongst other things,may serve as a repository for storing data that is processed, received,or generated as a result of the execution of one or more modules in themodule(s) 106 and the modules associated with the trip analysis unit120.

Although the repository 110 is shown internal to the system 100, it willbe noted that, in alternate embodiments, the repository 110 can also beimplemented external to the computing device 100, where the repository110 may be stored within a database (not shown in FIG. 1)communicatively coupled to the computing device 100. The data containedwithin such external database may be periodically updated. For example,new data may be added into the database (not shown in FIG. 1) and/orexisting data may be modified and/or non-useful data may be deleted fromthe database (not shown in FIG. 1). In one example, the data may bestored in an external system, such as a Lightweight Directory AccessProtocol (LDAP) directory and a Relational Database Management System(RDBMS).

The trip analysis unit 120, executed by the one or more hardwareprocessors of the system 100, receives a plurality of driving datapertaining to the journey from a plurality of Global Positioning System(GPS) sensors. The plurality of driving data includes a timestamp, alateral acceleration of a vehicle, a longitudinal acceleration of thevehicle and a plurality of traffic information associated with thejourney.

Further, the trip analysis unit 120, executed by the one or morehardware processors of the system 100, segments the journey into aplurality of sub-journeys based on the plurality of traffic information.The plurality of traffic information includes a capacity of a road,heavy traffic hours, types of vehicles, number of vehicles, and vehicledensity per unit length of road. In an embodiment, the plurality oftraffic information is obtained from the global traffic database. Forexample, point A to point B on a road is considered as one completejourney A-B. The journey A-B is segmented into the plurality ofsub-journeys (A-B1, B1-B2, . . . , B(N−1)-BN, BN-B, where N+1 is thenumber of segments). Each sub-journey is associated with a plurality ofdriving events.

Further, the trip analysis unit 120, executed by the one or morehardware processors of the system 100, detects the plurality of drivingevents for each of the plurality of sub-journeys based on the lateraland the longitudinal acceleration. The plurality of driving eventsincludes a lane change, an abrupt turning and an overtake.

Further, the trip analysis unit 120, executed by the one or morehardware processors of the system 100, computes an event score (m1)associated with each of the plurality of driving events corresponding toeach sub-journey based on the driving data. The event score (m1) iscalculated based on the number of each event (number of times an eventoccurs) corresponding to a sub-journey. For example, the trip/journey ABis segmented into 5 sub-journey A-B1, B1-B2, B2-B3, B3-B4, B4-B. Out ofwhich 2 sub-journeys belong to Turning event and 3 sub-journeys belongto lane changing event according to global database. The lane changingevent score for the sub-journey A-B1 can be 3, wherein the lane changingevent score 3 indicates that, there are 3 lane changes happened in thesub-journey A-B1. The score associated with each driving event is mappedaccording to number of Turnings and lane changing performed by thesubject driver on journey AB.

Further, the trip analysis unit 120, executed by the one or morehardware processors of the system 100, normalizes the event score (m1)corresponding to the plurality of driving events based on a percentilemapping. The percentile mapping is performed by comparing the eventscore and global statistics.

In an embodiment, the normal distribution F1 (μ=x1, σ=y1) for LaneChange event taken from the global database with (x1) number of averagelane changes and (y1) standard deviation. The lane change indicates aseries of moves taken while driving through a sub-journey with requiredskill and attention. The terms “segment” and “sub-journey” are usedinterchangeably throughout the document. The subject (driver) performsan average (x2) lane changes with (y2) deviation during the journeynormally distributed in F2 (μ=x2, σ=y2). Further, a comparison isperformed between the data captured during surveys (F2) and the globalstatistics (F1). For example, (m1) is the original score of lanechanging by subject (D1) in F1, which has a z-score (z1)=(m1−x1)/y1. Thez1 is mapped in F2 designed from the surveys to obtain the percentilescore (p)=(z1*y2)₊x2. The mapping provides the relative feasibility andcomparison of (m1) in F2 for fuzzy classification. Similarly, the score(p) for other two events Turning and Overtake in same sub-journey iscalculated and (p) is rounded off to get the exact number of lanechanges in F2 mapping.

There are 2 cases that are possible based on value of (p):

Case 1: If (p) is less than (m1), then (p) is taken as the percentilescore without any modification.

Case 2: If (p) is greater than (m1), then we have incorporated Gaussiannoise in the data to adjust the number of lane changes.

In an example embodiment, there are two normal distributions one fromthe global database as depicted in Table 1 and from the drivers (drivingsurvey data from GPS) as depicted in Table 2.

TABLE 1 Driving Events (F1) Mu(x1) Sigma(y1) Lane Changing 6 3 Turning 72 Overtake 2 1

TABLE 2 Driving Events (F2) Mu(x2) Sigma(y2) Lane Changing 3 2 Turning 73 Overtake 4 1Case1: Let, driver D1 perform number of lane changes, m1=5 in SegmentS1.From Table 1, Row1: z1=(5−6)/3=−0.33From Table 2, Row1: p=(−0.33*2)+3=2.33=2 (Rounded off).Since p<m1, Taken as it is.Case2: Let, driver D1 perform number of turns, m1=7 in Segment S1.From Table 1, Row2: z1=(7−7)/2=0From Table 2, Row2: p=(0*3)+7=7Since p=m1, Taken as it is.Case3: Let, driver D1 performs number of overtakes, m1=3 in Segment S1.From Table 1, Row3: z1=(3−2)/1=1From Table 2, Row3: p=(1*1)+4=5Since p>m1, Gaussian noise is introduced to adjust p.

The similar analysis is performed for all sub-journeys. The overallacceleration ABNew for the complete journey is calculated by mergingdata associated with all sub-journey together. The calculation isperformed separately on both lateral acceleration and longitudinalacceleration.

Further, the trip analysis unit 120, executed by the one or morehardware processors of the system 100, classifies, the journey into atleast one of very good, good, average, bad and very bad, based on thenormalized event score by utilizing fuzzy inference system.

In an embodiment, a plurality of inputs for the fuzzy inference systemare defined. For example, the input H.BRAKE (p) depicts the percentageof harsh brakes in the trip, CRUISE (q) depicts the percentage of triptravelled smoothly at an economical speed and H.ACC(r) depicts thepercentage of harsh driving in the trip. Output of the present fuzzyinference system is represented as JOURNEY.TY P (V) which depicts theoverall quality of driving in the whole journey (trip).

In an embodiment, fuzzification is performed on the plurality of inputs.The fuzzification includes mapping of the plurality of inputs to thecorresponding linguistic variables. The linguistic variables are givenby fuzzy sets as. H.BRAKE=[L, M, H], CRUISE=[L, M, H] and H.ACC=[L, M,H], where L=Low, M=Medium and H=High. Further, the linguistic variablesbreak down the crisp values in a plurality of linguistic variable rangesbetween 0 and 1. The plurality of linguistic variable ranges are furtherassociated with a certain degree of membership.

In an embodiment, the representation of inputs are performed bytriangular membership function. FIG. 2 depicts membership function plotfor the input variable H.BRAKE for the method of trip classification, inaccordance with some embodiments of the present disclosure. Other twoinputs are represented in a similar manner as the first input H.BRAKE.

In an embodiment, the Linguistic variables in all the fuzzy sets withtheir respective fuzzy numbers are depicted in Table 3.

TABLE 3 Inputs LOW Medium High H.BRAKE 0, 0, 0.014 0.0035, 0.0175,0.021, 0.035, 0.0315 0.035 CRUISE 0.75, 0.75, 0.85 0.775, 0.875, 0.9750.9, 1, 1 H.ACC 0, 0, 0.014 0.0035, 0.0175, 0.021, 0.035, 0.0315 0.035

TABLE 4 Rule No. H.BRAKE CRUISE H.ACC JOURNEY.TY P 1 L L L AVG 2 L L MAVG 3 L L H AVG 4 L M L AVG 5 L M M AVG 6 L M H AVG 7 L H L VG 8 L H M G9 L H H AVG 10 M L L AVG 11 M L M AVG 12 M L H B 13 M M L AVG 14 M M MAVG 15 M M H AVG 16 M L H G 17 M H M AVG 18 M H H AVG 19 H L L AVG 20 HL M B 21 H L H VB 22 H M L AVG 23 H M M AVG 24 H M H AVG 25 H H L AVG 26H H M AVG 27 H H H AVG

Table 4 depicts a rule base. In an embodiment, rule-base for the system100 is constructed on the basis of general experience and the commonsense of a user. Rules are constructed as “If” and “Then” statements.For example, rule 1 from 1st row of Table 4 is like “If H.BRAKE is Lowand CRUISE is Low and H.ACC is Low, then JOURNEY.TY P is AVG(Average)”.Similarly, a plurality of rules are derived. The plurality of rules arecombined by using the logic operator “AND” which means the “Minimum”.Hence, firing strength of each rule is defined by the minimum of thethree input membership values. The consequence of each rule is obtainedby combining the firing strength and the output membership function,aggregation of all 2 the consequences will produces the outputdistribution. The defuzzification of output distribution is done bycentroidal method which provide the crisp value of the output.

In an embodiment, the output fuzzy set is defined as JOURNEY.TYP=[VG=VeryGood, G=Good, AVG=Average, B=Bad, VB=VeryBad], and fuzzynumbers for all variables represented by triangular membership functionare provided in Table 5.

TABLE 5 Output VG G AVG B VB JOURNEY.TY P 0, 0, 0.25, 0.5, 0.75, 0, 0.250.25, 0.5 0.5, 0.75 0.75, 1 1, 1

FIG. 3 depicts membership function plot for the output variable formethod of trip classification, in accordance with some embodiments ofthe present disclosure.

In an embodiment, trip analysis is performed on 38 drivers, each driverhas driven certain number of trips for some duration. In total, 3927trips has been analyzed by the present disclosure and classified.Comparison between the results obtained from the fuzzy inference systemand the density plots of acceleration of some trips of the driver aredepicted in FIGS. 4 and 5.

FIG. 4 depicts density plots for the acceleration of GOOD, AVERAGE andBAD trip for the method for trip classification, in accordance with someembodiments of the present disclosure. Now referring to FIG. 4, the plot402 indicates the GOOD acceleration, 406 indicates the AVERAGEacceleration and 408 indicates the BAD acceleration.

FIG. 5 depicts density plots for the acceleration of VERY GOOD and VERYBAD trip for the method for trip classification, in accordance with someembodiments of the present disclosure. Now referring to FIG. 5, the plot502 indicates the VERY GOOD acceleration and the plot 504 indicates theVERY BAD acceleration.

In an embodiment, table 6 depicts the crisp values of all the inputs andthe obtained fuzzy output with respective inference of Good, Average andBad trip as plotted in FIG. 4.

TABLE 6 Fuzzy H.BRAKE CRUISE H.ACC JOURNEY.TY P Inference 0.006 0.96810.0046 0.2399 Good 0.0074 0.918 0.0066 0.4866 Average 0.0342 0.83330.0223 0.7067 Bad

From Table 6, if the crisp values of inputs are H.BRAKE=0.006,CRUISE=0.9681 and H.ACC=0.0046 then, according to inference systemJOURNEY.TY P=0.2399. This value is mapped in range of Good which meansthe quality of driving in this trip is good. Same we can see in FIG. 3that the highest number of economical acceleration is concentrated,which is considered to be as a good trip. Similarly, results for Averageand Bad trip can also be compared.

In an embodiment, table 7 indicates the crisp values of all the inputsand the obtained fuzzy output with respective inference of Very Good andVery Bad trip are plotted in FIG. 5.

TABLE 7 Fuzzy H.BRAKE CRUISE H.ACC JOURNEY.TY P Inference 0 0.9681 00.0800 Very Good 0.0554 0.8364 0.0677 0.9000 Very Bad

FIG. 6 is a flow diagram for a processor implemented method for tripclassification, in accordance with some embodiments of the presentdisclosure. The method 600 may be described in the general context ofcomputer executable instructions. Generally, computer executableinstructions can include routines, programs, objects, components, datastructures, procedures, modules, functions, etc., that performparticular functions or implement particular abstract data types. Themethod 400 may also be practiced in a distributed computing environmentwhere functions are performed by remote processing devices that arelinked through a communication network. The order in which the method600 is described is not intended to be construed as a limitation, andany number of the described method blocks can be combined in any orderto implement the method 600, or an alternative method. Furthermore, themethod 600 can be implemented in any suitable hardware, software,firmware, or combination thereof. At 602, the system 100, receives, by aone or more hardware processors, the plurality of driving datapertaining to the journey from the plurality of Global PositioningSystem (GPS) sensors. The plurality of driving data comprising atimestamp, a lateral acceleration of a vehicle, a longitudinalacceleration of the vehicle and a plurality of traffic informationassociated with the journey. At 604, the system 100 Segment, by the oneor more hardware processors, the journey into a plurality ofsub-journeys based on the plurality of traffic information. Theplurality of traffic information includes the capacity of the road,heavy traffic hours, types of vehicles, number of vehicles and vehicledensity per unit length of road. The plurality of traffic information isobtained from a global database. At 606, the system 100 detects, by theone or more hardware processors, a plurality of driving events for eachof the plurality of sub-journeys based on the lateral and longitudinalacceleration; wherein the plurality of driving events comprising a lanechange, an abrupt turning and an overtake. At 608, the system 100computes, by the one or more hardware processors, an event scoreassociated with each of the plurality of driving events corresponding toeach sub-journey based on the driving data. At 610, the system 100normalizes, by the one or more hardware processors, the event scorecorresponding to the plurality of driving events based on a percentilemapping, wherein the percentile mapping is performed by comparing theevent score and a global statistics. At 612, the system 100 classifies,by the one or more hardware processors, the journey into at least one ofvery good, good, average, bad and very bad, based on the normalizedevent score by utilizing fuzzy classification.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

The embodiments of present disclosure herein addresses unresolvedproblem of end to end trip classification. Further, the presentdisclosure can be utilized to improve transportation safety and thedriving experience as a whole. Furthermore, the system 100 can beincorporated into Advanced Driver Assistance System (ADAS). By couplingsensing information with accurate event prediction, the ADAS can preventaccidents by warning the driver ahead of time of potential danger byclassifying the driving.

It is to be understood that the scope of the protection is extended tosuch a program and in addition to a computer-readable means having amessage therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software modules located therein. Thus, themeans can include both hardware means and software means. The methodembodiments described herein could be implemented in hardware andsoftware. The device may also include software means. Alternatively, theembodiments may be implemented on different hardware devices, e.g. usinga plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various modules described herein may be implemented in other modulesor combinations of other modules. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan comprise, store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments. Also, the words “comprising,”“having,” “containing,” and “including,” and other similar forms areintended to be equivalent in meaning and be open ended in that an itemor items following any one of these words is not meant to be anexhaustive listing of such item or items, or meant to be limited to onlythe listed item or items. It must also be noted that as used herein andin the appended claims, the singular forms “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e. non-transitory. Examples include random accessmemory (RAM), read-only memory (ROM), volatile memory, nonvolatilememory, hard drives, CD ROMs, DVDs, flash drives, disks, and any otherknown physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A processor implemented method, the methodcomprising: receiving, by one or more hardware processors, a pluralityof driving data pertaining to a journey from a plurality of GlobalPositioning System (GPS) sensors, wherein the plurality of driving datacomprising a timestamp, a lateral acceleration of a vehicle, alongitudinal acceleration of the vehicle and a plurality of trafficinformation associated with the journey; segmenting, by the one or morehardware processors, the journey into a plurality of sub-journeys basedon the plurality of traffic information; detecting, by the one or morehardware processors, a plurality of driving events for each of theplurality of sub-journeys based on the lateral acceleration and thelongitudinal acceleration; computing, by the one or more hardwareprocessors, an event score associated with each of the plurality ofdriving events corresponding to each sub-journey based on the drivingdata; normalizing, by the one or more hardware processors, the eventscore corresponding to the plurality of driving events based on apercentile mapping, wherein the percentile mapping is performed bycomparing the event score and a global statistics; and classifying, bythe one or more hardware processors, the journey into at least one ofvery good, good, average, bad and very bad, based on the normalizedevent score by utilizing fuzzy classification.
 2. The processorimplemented method of claim 1, wherein the plurality of trafficinformation comprising a capacity of a road, heavy traffic hours, typesof vehicles, number of vehicles and vehicle density per unit length ofroad.
 3. The processor implemented method of claim 1, wherein theplurality of traffic information is obtained from a global database. 4.The processor implemented method of claim 1, wherein the plurality ofdriving events comprising a lane change, an abrupt turning and anovertake.
 5. A system (100) comprising: at least one memory (104)storing programmed instructions; and one or more hardware processors(102) operatively coupled to the at least one memory, wherein the one ormore hardware processors (102) are capable of executing the programmedinstructions stored in the at least one memory (104) to: receive aplurality of driving data pertaining to a journey from a plurality ofGlobal Positioning System (GPS) sensors, wherein the plurality ofdriving data comprising a timestamp, a lateral acceleration of avehicle, a longitudinal acceleration of the vehicle and a plurality oftraffic information associated with the journey; segment the journeyinto a plurality of sub-journeys based on the plurality of trafficinformation; detect a plurality of driving events for each of theplurality of sub-journeys based on the lateral and the longitudinalacceleration; compute an event score associated with each of theplurality of driving events corresponding to each sub-journey based onthe driving data; normalize the event score corresponding to theplurality of driving events based on a percentile mapping, wherein thepercentile mapping is performed by comparing the event score and aglobal statistics; and classify, the journey into at least one of verygood, good, average, bad and very bad, based on the normalized eventscore by utilizing fuzzy classification.
 6. The system of claim 5,wherein the plurality of traffic information comprising a capacity of aroad, heavy traffic hours, types of vehicles, number of vehicles andvehicle density per unit length of road.
 7. The system of claim 5,wherein the plurality of traffic information is obtained from a globaldatabase.
 8. The system of claim 5, wherein the plurality of drivingevents comprising a lane change, an abrupt turning and an overtake. 9.One or more non-transitory machine readable information storage mediumscomprising one or more instructions which when executed by one or morehardware processors causes: receiving, by one or more hardwareprocessors, a plurality of driving data pertaining to a journey from aplurality of Global Positioning System (GPS) sensors, wherein theplurality of driving data comprising a timestamp, a lateral accelerationof a vehicle, a longitudinal acceleration of the vehicle and a pluralityof traffic information associated with the journey; segmenting, by theone or more hardware processors, the journey into a plurality ofsub-journeys based on the plurality of traffic information; detecting,by the one or more hardware processors, a plurality of driving eventsfor each of the plurality of sub-journeys based on the lateralacceleration and the longitudinal acceleration; computing, by the one ormore hardware processors, an event score associated with each of theplurality of driving events corresponding to each sub-journey based onthe driving data; normalizing, by the one or more hardware processors,the event score corresponding to the plurality of driving events basedon a percentile mapping, wherein the percentile mapping is performed bycomparing the event score and a global statistics; and classifying, bythe one or more hardware processors, the journey into at least one ofvery good, good, average, bad and very bad, based on the normalizedevent score by utilizing fuzzy classification.